Sparse recovery based compressive sensing algorithms for diffuse optical tomography

Dileep, B.P.V.; Dutta, Pranab; Prasad, P. M. K.; Santhosh, M.

doi:10.1016/j.optlastec.2020.106234

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…Sparse recovery problems have found applications in the fields of compressed sensing [EK09], signal denoising [GBK16], optical imaging [DDPS20], machine learning [Yan13], and more.…”

Section: Overview Of Sparse Recovery Algorithms and Our Contributionsmentioning

confidence: 99%

Thresholding Greedy Pursuit for Sparse Recovery Problems

Le,

Novikov

2021

Preprint

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We study here sparse recovery problems in the presence of additive noise. We analyze a thresholding version of the CoSaMP algorithm, named Thresholding Greedy Pursuit (TGP). We demonstrate that an appropriate choice of thresholding parameter, even without the knowledge of sparsity level of the signal and strength of the noise, can result in exact recovery with no false discoveries as the dimension of the data increases to infinity.

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“…Sparse recovery problems have found applications in the fields of compressed sensing [EK09], signal denoising [GBK16], optical imaging [DDPS20], machine learning [Yan13], and more.…”

Section: Overview Of Sparse Recovery Algorithms and Our Contributionsmentioning

confidence: 99%

Thresholding Greedy Pursuit for Sparse Recovery Problems

Le,

Novikov

2021

Preprint

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“…Imaging with randomly scattered light is a significant challenge with a pressing need in non-invasive biomedical diagnosis [ 1 , 2 , 3 , 4 , 5 , 6 ]. One of the more significant applications is a non-invasive functional imaging technique called diffuse optical tomography (DOT), which uses near-infrared (NIR) light to map in 3D the optical characteristics of tissue by penetrating it deeply [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. Soft tissue, including the breast and the brain, can be penetrated several centimeters by diffuse light in the NIR wavelength range.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming

2023

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Diffuse optical tomography (DOT) is a non-invasive method for detecting breast cancer; however, it struggles to produce high-quality images due to the complexity of scattered light and the limitations of traditional image reconstruction algorithms. These algorithms can be affected by boundary conditions and have a low imaging accuracy, a shallow imaging depth, a long computation time, and a high signal-to-noise ratio. However, machine learning can potentially improve the performance of DOT by being better equipped to solve inverse problems, perform regression, classify medical images, and reconstruct biomedical images. In this study, we utilized a machine learning model called “XGBoost” to detect tumors in inhomogeneous breasts and applied a post-processing technique based on genetic programming to improve accuracy. The proposed algorithm was tested using simulated DOT measurements from complex inhomogeneous breasts and evaluated using the cosine similarity metrics and root mean square error loss. The results showed that the use of XGBoost and genetic programming in DOT could lead to more accurate and non-invasive detection of tumors in inhomogeneous breasts compared to traditional methods, with the reconstructed breasts having an average cosine similarity of more than 0.97 ± 0.07 and average root mean square error of around 0.1270 ± 0.0031 compared to the ground truth.

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“…So, it is approximated by reduced representation. DCT [16] is considered as the discrete-time version of the fourier cosine series as shown mathematically (4). DCT estimates the real approximation of the signal with less coefficients.…”

Section: Introductionmentioning

confidence: 99%

Comparison of different sparse dictionaries for compressive sampling

Devendrappa¹,

Karthik²,

A.³

et al. 2022

Bulletin EEI

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Compressive sampling/compressed sensing (CS) is building on the observation that most of the signals in nature are sparse or compressible concerning some transform domain. And by converse, the same can be reconstructed with high accuracy by making use of far fewer samples than what is required by violating Shannon-Nyquist theorem. Some of the transform techniques like discrete cosine transform, fast fourier transforms discrete wavelet transform, discrete fourier transforms. In this paper, novel CS techniques like FFTCoSAMP, DCTCoSaMP, and DWTCoSaMP are introduced and compared on different sparse transforms for CS in magnetic resonance (MR) images based on sparse signal sequences/dictionaries by means of transform techniques with respect to objective quality assessment algorithms like PSNR, SSIM and RMSE, where CoSaMP stands for compressive sampling matching pursuit. DWTCoSaMP is giving the PSNR values of 37.16 (DB4), 38.12 (Coif3), 38.5 (Sym8), for DCTCoSaMP and FFTCoSaMP, it’s 36.33 and 36.01 respectively. For DWTCoSaMP, SSIM value is 0.81, and for DCTCoSaMP and FFTCoSaMP, it’s 0.73 and 0.7 respectively. And finally, for DWTCoSaMP, RMSE value is 0.66, and for DCTCoSaMP and FFTCoSaMP, it’s 0.53 and 0.41 respectively. DWTCoSaMP reveals the best than rest of the methods and traditional CS techniques by the detailed comparison and analysis.

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Sparse recovery based compressive sensing algorithms for diffuse optical tomography

Cited by 7 publications

References 29 publications

Thresholding Greedy Pursuit for Sparse Recovery Problems

Thresholding Greedy Pursuit for Sparse Recovery Problems

Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming

Comparison of different sparse dictionaries for compressive sampling

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